code: def __init__(
      self, input_tensor_spec, num_atoms=51, num_actions=2, name=None
  ):
      self._num_atoms = num_atoms
      self._num_actions = num_actions
      super(DummyCategoricalNet, self).__init__(
          input_tensor_spec=input_tensor_spec, state_spec=(), name=name
      )
  
      # In CategoricalDQN we are dealing with a distribution over Q-values, which
      # are represented as num_atoms bins, ranging from min_q_value to
      # max_q_value. In order to replicate the setup in the non-categorical
      # network (namely, [[2, 1], [1, 1]]), we use the following "logits":
      # [[0, 1, ..., num_atoms-1, num_atoms, 1, ..., 1],
      #  [1, ......................................, 1]]
      # The important bit is that the first half of the first list (which
      # corresponds to the logits for the first action) place more weight on the
      # higher q_values than on the lower ones, thereby resulting in a higher
      # value for the first action.
      weights_initializer = np.array(
          [
              np.concatenate(
                  (np.arange(num_atoms), np.ones(num_atoms))
              ),
              np.concatenate((np.ones(num_atoms), np.ones(num_atoms))),
          ]
      )
      kernel_initializer = tf.constant_initializer(weights_initializer)
      bias_initializer = tf.keras.initializers.Ones()
  
      # Store custom layers that can be serialized through the Checkpointable API.
      self._dummy_layers = []
      self._dummy_layers.append(
          tf.keras.layers.Dense(
              num_actions * num_atoms,
              kernel_initializer=kernel_initializer,
              bias_initializer=bias_initializer,
          )
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import os
  from absl import flags
  import numpy
  import tensorflow
  from tf_agents.networks import categorical_q_network
  from tf_agents.networks import network
  from tf_agents.policies import categorical_q_policy
  from tf_agents.policies import policy_saver
  from tf_agents.specs import tensor_spec
  from tf_agents.trajectories import time_step
  from tf_agents.utils import test_utils
label_desc: Use image node_id 1 to create a new DummyCategoricalNet object from inherited base classes: network with example: obj = DummyCategoricalNet(input_tensor_spec, num_atoms, num_actions, name)